Fischer, Manfred M.; Gopal, Sucharita
Leaming in neural networks has attracted considerable interest in recent years. Our focus is
on learning in single hidden layer feedforward networks which is posed as a search in the
network parameter space for a network that minimizes an additive error function of
statistically independent examples. In this contribution, we review first the class of single
hidden layer feedforward networks and characterize the learning process in such networks
from a statistical point of view. Then we describe the backpropagation procedure, the leading
case of gradient descent learning algorithms for the class of networks considered here, as
well as an efficient heuristic modification. Finally, we analyse the applicability...
(application/pdf) - 05-may-2018